Polynomial Based Linear Regression Model to Predict COVID-19 Cases

Nikhil, Arushi Saini, Santu Panday, Neha Gupta
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引用次数: 3

Abstract

The epidemic COVID-19 has profoundly influenced people's wellness worldwide and the number of fatalities from diseases continues to increase world-wide. Despite technology's remarkable success in our daily lives, notably in ML and DL, AI also helped humanity fight the grueling COVID-19 war. DL is only one approach of ensuring that potential data-driven technologies can help humankind manage COVID-19. Big data and artificial intelligence are used to leverage exceptional efforts to combat the COVID-19 pandemic crisis. In some prior disease outbreaks, various AI offshoots were deployed. AI was applied in the identification of disease clusters, case monitoring, future outbreak predictions, mortality risk, and diagnosis of COVID-19, resource allocation illness management, training facilitation, record maintaining and design identification for the investigation of the trend towards the illness. AI & Machine learning can help to find out the strategies to prevent the Corona virus. This paper presents a polynomial based linear regression model to predict the future cases according to the current situation using data of last few months, showing the output on the graph. The paper also discusses the applications of AI & Machine learning in Corona virus pandemic like forecasting infection rate, diagnose with images comprehensively and will also discuss the role of Machine learning in facilitating the development of vaccine as well.
基于多项式的线性回归模型预测COVID-19病例
2019冠状病毒病疫情深刻影响了全世界人民的健康,全球因疾病死亡的人数继续增加。尽管技术在我们的日常生活中取得了巨大的成功,尤其是在机器学习和深度学习方面,但人工智能也帮助人类打赢了艰苦的COVID-19战争。深度学习只是确保潜在的数据驱动技术能够帮助人类应对COVID-19的一种方法。利用大数据和人工智能为应对COVID-19大流行危机做出了非凡的努力。在以前的一些疾病暴发中,部署了各种人工智能分支。人工智能应用于疾病聚集性识别、病例监测、未来疫情预测、死亡风险和COVID-19诊断、资源分配疾病管理、培训便利、记录维护和设计识别,以调查疾病趋势。人工智能和机器学习可以帮助找到预防冠状病毒的策略。本文利用最近几个月的数据,提出了一个基于多项式的线性回归模型,根据目前的情况预测未来的案例,并在图上显示输出。本文还讨论了人工智能和机器学习在冠状病毒大流行中的应用,如预测感染率,综合图像诊断,并讨论了机器学习在促进疫苗开发中的作用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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